Grid Operations and Energy Trading in Power & Utilities

AI-driven demand forecasting and renewable integration optimizing generation scheduling, reducing curtailment, and enhancing grid stability with variable renewables

The challenge

A utility with 35% renewable penetration struggled with demand-supply balancing, experiencing 8-12% annual renewable curtailment and relying on expensive peaking plants. Legacy models failed to capture weather-demand-renewable relationships, costing $15-20M annually.

The solution

We implemented AI Forecasting Platform using Azure Machine Learning with LSTM and gradient boosting models. Databricks processed weather, AMI, and generation data for renewable-demand predictions, with Power BI enabling traders to visualize forecasts and dispatch recommendations.

Measured impact

3.5-4.2%

Improvement in demand forecast accuracy (MAPE reduction),

60%

Reduction in renewable energy curtailment,

$12-16M

Annual savings from optimized generation dispatch